[特邀报告]Traits and Trends of AI in Medical Imaging - 报告详情

Traits and Trends of AI in Medical Imaging
编号:127 访问权限:仅限参会人 更新:2021-11-09 17:04:41 浏览:232次 特邀报告

报告开始:2021年11月14日 10:45 (Asia/Shanghai)

报告时间:25min

所在会议:[PS2] Plenary Session 2 & CT Session [SunMS] Sunday Morning Session

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摘要
 Artificial intelligence or deep learning technologies have gained prevalence in solving medical imaging tasks. In this talk, we first review the traits that characterize medical images, such as multi-modalities, heterogeneous and isolated data, sparse and noisy labels, imbalanced samples. We then point out the necessity of a paradigm shift from "small task, big data" to "big task, small data". Finally, we illustrate the trends of AI technologies in medical imaging and present a multitude of algorithms that attempt to address various aspects of “big task, small data”:
  • Annotation-efficient methods that tackle medical image analysis without many labelled instances, including one-shot or label-free inference approaches.
  • Universal models that learn “common + specific” feature representations for multi-domain tasks to unleash the potential of ‘bigger data’, which are formed by integrating multiple datasets associated with tasks of interest into one use.
  • "Deep learning + knowledge modeling" approaches, which combine machine learning with domain knowledge to enable start-of-the-art performances for many tasks of medical image reconstruction, recognition, segmentation, and parsing.
关键字
报告人
S. Kevin Zhou
Professor University of Science and Technology of China

S. Kevin Zhou
Professor, University of Science and Technology of China

  • Executive Dean, School of Biomedical Engineering
  • Director, Center for Medical Imaging, Robotics, Analytic Computing, Learning & Engineering (MIRACLE)
  • Fellow of American Institute for Medical and Biological Engineering (AIMBE)
  • Fellow of IEEE
  • Fellow of National Academy of Investors (NAI)

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重要日期

摘要提交日期:

2021/08/31

2021/10/25

全文投稿日期:  

2021/09/15

2021/10/25

录取通知日期: 

2021/09/30

2021/11/01

会议日期:   2021-11-12-2021-11-14

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